3 ms·

But as my sample size grows as I work for more companies by probability the data becomes more and more inline with the actual population. Who's to say my sampl

But as my sample size grows as I work for more companies by probability the data becomes more and more inline with the actual population. Who's to say my sample isn't a good representation of the actual population? Edit: in response to your edit of quadrants. I never said they don't exist. I stated that there are correlations. And I explicitly stated that despite the correlations I've seen examples of all types of programmers. Even programmers outside of the correlations. Of course as stated before, these correlations are anecdotal. Just my experience. I don't think it's necessarily invalid despite that.

fragmede 3y ago
Simply on the basis that sampling bias exists, I'll say your sample isn't a good representation of the actual population. Presuming you're human and haven't lived multiple lives simultaneously, it's just not possible you've worked in every subfield of our industry, meeting every possible variation of programmer. You didn't get your current job from first principles. There's bias, just from job-1 on how you got your current job, nevermind your entire whole background as a human being. A coder in India lives in a wholly different culture than someone in Eastern Europe is different from a North Korean is different than a San Francisican programmer, never mind the actual work they're doing; firmware, web crud, kernel, LLM. Different types are going to be more prevalent in different areas, and unless you've got a truly random sampling of workplaces, there's gonna be some bias in action.
- ninetyninenine 3y ago
  >Simply on the basis that sampling bias exists, I'll say your sample isn't a good representation of the actual population. I would say it's possible. But also every person I've ever met for the most part has 2 eyes. Despite sampling bias my measurement is pretty accurate to the population that most humans have two eyes. So just because bias exists doesn't render a measurement inaccurate. It could still be accurate. In fact I doubt even the most rigorous science can be completely unbiased. You can't say just because bias exists the measurement is inaccurate. It could be accurate. A measurement existing opens the possibility up for the measurement to be accurate and true to the real world scenario.
  - fragmede 3y ago
    For something as basic as number of eyes, you make a valid point, but what's more likely? That your career choices have been free from any bias which makes your sample accurate, or that there's many, many biases, from the culture of where you born to the programming language you first learned, to how rich your parent were, that have lead you to have the career that you have, resulting in a skewed sample?
    - ninetyninenine 3y ago
      I think what you say is valid. But I also believe that there is a realistic chance that my sample is accurate despite the biases. It also makes sense logically. A smarter person will be faster at reading sloppy code.
      - fragmede 3y ago
        as a sloppy coder (until I clean it up for review) I appreciate the compliment!
saint_fiasco 3y ago
> I stated that there are correlations The funny part, the reason it's called a paradox, is that the correlation can be fake or can even be reversed. We cannot know either way just from our personal experiences, the selection effects are too strong. And the anecdote you tell, the part about smart programmers being smart enough to get away with being a little messy, and dumb programmers being obsessed with cleanliness to reduce cognitive load, are exactly the sorts of situations that can cause the paradox. It's like when you are casually dating a bunch of people and find that the good-looking ones tend to have bad personalities because they are attractive enough that they can get away with it. It is a real correlation from your perspective, but on a global scale the correlation is opposite. Good looking people tend to be more charismatic in real life, we just can't tell while we are on the dating market because the ones who are both good looking and charismatic are out of our league, and as for the ugly/mean ones, we are out of their league.
- ninetyninenine 3y ago
  True. But how do you know the paradox applies here? What if my sample is accurate? What I don't get is how someone can out of nowhere say the paradox applies in my case. I mean this paradox can apply to any measurement even the most rigorous experiment. So why suddenly come to my post and bring up this application?

fragmede 3y ago

Simply on the basis that sampling bias exists, I'll say your sample isn't a good representation of the actual population. Presuming you're human and haven't lived multiple lives simultaneously, it's just not possible you've worked in every subfield of our industry, meeting every possible variation of programmer. You didn't get your current job from first principles. There's bias, just from job-1 on how you got your current job, nevermind your entire whole background as a human being. A coder in India lives in a wholly different culture than someone in Eastern Europe is different from a North Korean is different than a San Francisican programmer, never mind the actual work they're doing; firmware, web crud, kernel, LLM. Different types are going to be more prevalent in different areas, and unless you've got a truly random sampling of workplaces, there's gonna be some bias in action.

ninetyninenine 3y ago
>Simply on the basis that sampling bias exists, I'll say your sample isn't a good representation of the actual population. I would say it's possible. But also every person I've ever met for the most part has 2 eyes. Despite sampling bias my measurement is pretty accurate to the population that most humans have two eyes. So just because bias exists doesn't render a measurement inaccurate. It could still be accurate. In fact I doubt even the most rigorous science can be completely unbiased. You can't say just because bias exists the measurement is inaccurate. It could be accurate. A measurement existing opens the possibility up for the measurement to be accurate and true to the real world scenario.
- fragmede 3y ago
  For something as basic as number of eyes, you make a valid point, but what's more likely? That your career choices have been free from any bias which makes your sample accurate, or that there's many, many biases, from the culture of where you born to the programming language you first learned, to how rich your parent were, that have lead you to have the career that you have, resulting in a skewed sample?
  - ninetyninenine 3y ago
    I think what you say is valid. But I also believe that there is a realistic chance that my sample is accurate despite the biases. It also makes sense logically. A smarter person will be faster at reading sloppy code.
    - fragmede 3y ago
      as a sloppy coder (until I clean it up for review) I appreciate the compliment!

saint_fiasco 3y ago

> I stated that there are correlations The funny part, the reason it's called a paradox, is that the correlation can be fake or can even be reversed. We cannot know either way just from our personal experiences, the selection effects are too strong. And the anecdote you tell, the part about smart programmers being smart enough to get away with being a little messy, and dumb programmers being obsessed with cleanliness to reduce cognitive load, are exactly the sorts of situations that can cause the paradox. It's like when you are casually dating a bunch of people and find that the good-looking ones tend to have bad personalities because they are attractive enough that they can get away with it. It is a real correlation from your perspective, but on a global scale the correlation is opposite. Good looking people tend to be more charismatic in real life, we just can't tell while we are on the dating market because the ones who are both good looking and charismatic are out of our league, and as for the ugly/mean ones, we are out of their league.

ninetyninenine 3y ago
True. But how do you know the paradox applies here? What if my sample is accurate? What I don't get is how someone can out of nowhere say the paradox applies in my case. I mean this paradox can apply to any measurement even the most rigorous experiment. So why suddenly come to my post and bring up this application?